Digital Twins (DT) are discussed and implemented in various fields and industries as they provide a cost-efficient way of identifying decisions’ consequences. Various studies mention the potential positive effects of DTs on city planners and subsequently citizens, as new policies can be tested and the effect on travel patterns, congestion etc. can be explored. However, compared to other areas, such as manufacturing, DTs in public transport are less researched but emergingly discussed given its potentials for monitoring and forecasting. One challenge to overcome is the high development costs and the uncertainty of a single party as to whether the benefits will recoup costs. In contrast to manufacturing companies, it is often hard to argue for such a DT solution in the public sector, as the benefit for a single party may not cover the total costs. In order to create a cost-efficient DT solution in the public transport domain it is important to utilize a technological framework that is adaptable and extendable to include other parties and their use cases (e.g., individual transport, noise, CO2 emission, etc.). 

This work will analyze existing DTs in other areas, especially regarding the technological architecture used and its extensibility to other use cases. The methods and tools assessed as suitable are implemented and linked in a pipeline to create a DT for public transport. These tools include CityGML for the standardized representation of the study region, SUMO for the simulation of the public transport network and Cesium for the web-based interaction with the DT. The functionality of the pipeline and the tools included will be illustrated using a case study for the Kista region in Stockholm. In particular, the study exhibits the origin and processing of the input data, necessary for the prototypical development of a DT for public transportation. Furthermore, the study shows the use of vehicle movement data, in the form of Google Transit Feed Specification (GTFS) data, person movement from smartcard data and data on the physical infrastructure of the Stockholm authorities. Finally, the case study illustrates existing challenges, e.g., in terms of data requirements and development effort, and identifies potential solutions as well as future research directions.